Static and dynamic gestures are frequently used in activities supporting learning, recovery healthcare, engineering, and 3D games to increase the interactivity between man and machine. The gestures are detected via hardware devices and data is processed using different software methods. This paper presents the manner of detection and interpretation of two gestures, a hand rotation gesture and a palm closing and opening gesture, using the Leap Motion device. These two dynamic gestures are very

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... ten used in hand recovery exercises. For comparing the two gestures we use data classification methods, Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The data for the gesture classification were 80% training data and 20% testing data. The metrics for comparison are precision, recall, F1-score, and the total number of testing cases (support). The SVM classifier gives an accuracy of 99.4% and the MLP classifier a 96.2%. We built two confusion matrices for better visualizing the results.